The overall goal of the application is to utilize data from electronic health records (EHR) to detect diagnostic errors in primary care, understand their causes, and lay groundwork to formulate future prevention strategies. Diagnostic errors are likely the most common types of errors in primary care. They are also the most expensive and are the leading basis for malpractice claims. Despite their importance, diagnostic errors are an underemphasized and understudied area of patient safety, in part because they are difficult to detect. Strategies to help detect diagnostic errors are critical to improving quality and safety of primary care delivery. Detection methods such as error reporting systems and random chart reviews to identify diagnostic errors are limited. In recent years, computerized trigger techniques have been used to identify adverse events in other settings by selecting specific charts for more detailed review. In our preliminary work, we have developed and tested two computerized trigger tools to identify charts that may contain evidence of diagnostic errors in primary care. We believe that refining these tools and integrating them with additional variables could lead to a higher detection rate for diagnostic errors. In additional preliminary work, we tested a computerized method that could be used as a new trigger tool to detect diagnostic errors related to abnormal test result follow-up. We now propose to validate this potential trigger tool. We also propose to expand our research beyond the VA to a large primary care network in a practice based research network in Central Texas. These settings will include internal medicine and family medicine, academic and nonacademic practices, urban and rural patients, and significant racial, gender, ethnic, age, and socioeconomic diversity. Our specific aims are: 1) To apply and improve computerized triggers based on visit patterns to detect, measure, and learn from diagnostic errors in diverse primary care settings. 2) To test whether a method of computerized tracking for abnormal test results that are potentially lost to follow-up can be used as a trigger to identify diagnostic near-misses in primary care. In Aim 1 we will query clinical repositories with triggers and electronically collect additional clinical data (variables) about primary care visits. To test the utility of triggers, we will compare their positive predictive values (PPV) with controls, a random sample of visits that do not meet trigger criteria. Trained, blinded chart reviewers will verify the presence of diagnostic errors in the two subsets. To improve our trigger we will use a logistic regression model to test the additive PPV of integrating the trigger with specific independent clinical variables. In Aim 2, we will electronically track and identify records of patients for further chart reviews based on a new potential trigger. We will test the validity of this trigger tool for detecting diagnostic errors related to test results lost to follow-up by comparing the PPVs of the triggered and control subsets. Refining and validating triggers for diagnostic errors in diverse primary care settings would set the stage for their use nationally in quality measurement and improvement activities. [unreadable] [unreadable] [unreadable] [unreadable]